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<!doctype html>
<html>
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="chrome=1">
<title>Python-Sp16 by uiuc-cse</title>
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<body>
<div class="wrapper">
<header>
<h1 class="header">Python-Sp16</h1>
<p class="header">CSE Workshops in Python, Spring 2016</p>
<ul>
<li class="download"><a class="buttons" href="https://github.com/uiuc-cse/python-sp16/zipball/master">Download ZIP</a></li>
<li class="download"><a class="buttons" href="https://github.com/uiuc-cse/python-sp16/tarball/master">Download TAR</a></li>
<li><a class="buttons github" href="https://github.com/uiuc-cse/python-sp16">View On GitHub</a></li>
</ul>
<p class="header">This project is maintained by <a class="header name" href="https://github.com/uiuc-cse">uiuc-cse</a></p>
</header>
<section>
<p>All workshops will be held in the EWS computer laboratory, L440 Digital Computer Laboratory. There is no sign-up for this series—walk-ins are welcome and encouraged!</p>
<p>L440 DCL is a little tricky to find if you haven't been there before. It's located in the basement, and can be accessed by going down the main staircase in DCL and turning right.</p>
<p><img src="./img/map-l440.png" alt=""></p>
<p>For most of the lessons, we will require outside modules. We will provide a Python <em>virtual environment</em> in class, which will run on the EWS machine and let you access all of the modules required. <a href="https://store.continuum.io/cshop/anaconda/">Anaconda</a> is an excellent Python distribution for your personal machine.</p>
<p><strong>Please find any necessary setup directions for today's workshop below.</strong></p>
<h1>
<a id="introduction-to-python" class="anchor" href="#introduction-to-python" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Introduction to Python</h1>
<h4>
<a id="feb-16-noon200-pm" class="anchor" href="#feb-16-noon200-pm" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Feb. 16, Noon–2:00 p.m.</h4>
<ul>
<li> <a href="./lessons/intro-exercises.md">Exercises</a>
</li>
<li> <a href="./lessons/data/microarray.csv">Data set #1</a>
</li>
<li> <a href="./lessons/data/wheres-waldo-locations.csv">Data set #2</a>
</li>
</ul>
<h1>
<a id="scientific-computing-numpy-scipy" class="anchor" href="#scientific-computing-numpy-scipy" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Scientific Computing (<code>numpy</code>, <code>scipy</code>)</h1>
<h4>
<a id="feb-23-noon200-pm-numpy" class="anchor" href="#feb-23-noon200-pm-numpy" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Feb. 23, Noon–2:00 p.m. (<code>numpy</code>)</h4>
<h4>
<a id="feb-24-noon200-pm-scipy" class="anchor" href="#feb-24-noon200-pm-scipy" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Feb. 24, Noon–2:00 p.m. (<code>scipy</code>)</h4>
<ul>
<li>
<p>Open a new Terminal and run the setup code below to prepare your EWS machine to run our Python lesson today. This will download the data we will work with and the NumPy notebook. (You can copy and paste the entire block at once.)</p>
<pre><code>source /class/cs101/etc/venv/cse/bin/activate /class/cs101/etc/venv/cse/
cd
cd Desktop
git clone https://github.com/uiuc-cse/python-sp16.git
cd python-sp16
jupyter notebook
</code></pre>
</li>
<li><p>Find the <code>numpy-scipy.ipynb</code> notebook and open it in the browser.</p></li>
<li><p>To execute a code cell, select it and press <code>Shift</code>+<code>Enter</code>.</p></li>
</ul>
<h1>
<a id="data-analytics-pandas-seaborn" class="anchor" href="#data-analytics-pandas-seaborn" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Data Analytics (<code>pandas</code>, <code>seaborn</code>)</h1>
<h4>
<a id="mar-1-noon200-pm" class="anchor" href="#mar-1-noon200-pm" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Mar. 1, Noon–2:00 p.m.</h4>
<ul>
<li>
<p>Download the following files to your Desktop:</p>
<ul>
<li> <a href="./lessons/pandas-working.ipynb"><code>pandas-working.ipynb</code></a>
</li>
</ul>
</li>
<li>
<p>Open a new Terminal and run the setup code below to prepare your EWS machine to run our Python lesson today. This will download the data we will work with and the Pandas notebook. (You can copy and paste the entire block at once.)</p>
<pre><code>source /class/cs101/etc/venv/cse/bin/activate /class/cs101/etc/venv/cse/
cd ; cd Desktop
jupyter notebook
</code></pre>
</li>
<li><p>Find the <code>pandas-working.ipynb</code> notebook and open it in the browser.</p></li>
<li><p>To execute a code cell, select it and press <code>Shift</code>+<code>Enter</code>.</p></li>
</ul>
<h1>
<a id="plotting-matplotlib" class="anchor" href="#plotting-matplotlib" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Plotting (<code>matplotlib</code>)</h1>
<h4>
<a id="mar-8-noon200-pm" class="anchor" href="#mar-8-noon200-pm" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Mar. 8, Noon–2:00 p.m.</h4>
<ul>
<li>
<p>Download the following files to your Desktop folder:</p>
<ul>
<li> <a href="./lessons/matplotlib.ipynb"><code>matplotlib.ipynb</code></a>
</li>
</ul>
</li>
<li>
<p>Open a new Terminal and run the setup code below to prepare your EWS machine to run our Python lesson today. This will download the data we will work with and the Pandas notebook. (You can copy and paste the entire block at once.)</p>
<pre><code>source /class/cs101/etc/venv/cse/bin/activate /class/cs101/etc/venv/cse/
cd ; cd Desktop
jupyter notebook
</code></pre>
</li>
<li><p>Find the <code>matplotlib.ipynb</code> notebook and open it in the browser.</p></li>
<li><p>To execute a code cell, select it and press <code>Ctrl</code>+<code>Enter</code>.</p></li>
</ul>
<h1>
<a id="intermediate-topics-in-python" class="anchor" href="#intermediate-topics-in-python" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Intermediate topics in Python</h1>
<h4>
<a id="mar-15-noon200-pm" class="anchor" href="#mar-15-noon200-pm" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Mar. 15, Noon–2:00 p.m.</h4>
<ul>
<li>
<p>Download the following files to your Desktop folder:</p>
<ul>
<li> <a href="./lessons/oop-intro.ipynb"><code>oop-intro.ipynb</code></a>
</li>
</ul>
</li>
<li>
<p>Open a new Terminal and run the setup code below to prepare your EWS machine to run our Python lesson today. This will download the data we will work with and the Pandas notebook. (You can copy and paste the entire block at once.)</p>
<pre><code>source /class/cs101/etc/venv/cse/bin/activate /class/cs101/etc/venv/cse/
cd ; cd Desktop
jupyter notebook
</code></pre>
</li>
<li><p>Find the <code>oop-intro.ipynb</code> notebook and open it in the browser.</p></li>
<li><p>To execute a code cell, select it and press <code>Ctrl</code>+<code>Enter</code> (or <code>Shift</code>+<code>Enter</code> to advance).</p></li>
</ul>
<h1>
<a id="machine-learning-scikit-learn" class="anchor" href="#machine-learning-scikit-learn" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Machine Learning (scikit-learn)</h1>
<h4>
<a id="mar-29-noon200-pm" class="anchor" href="#mar-29-noon200-pm" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Mar. 29, Noon–2:00 p.m.</h4>
<ul>
<li>
<p>Download the following files to your Desktop folder:</p>
<ul>
<li> <a href="./lessons/satellite-kmeans.ipynb"><code>satellite-kmeans.ipynb</code></a>
</li>
<li> <a href="./lessons/scikit-learn.ipynb"><code>scikit-learn.ipynb</code></a>
</li>
</ul>
</li>
<li>
<p>Open a new Terminal and run the setup code below to prepare your EWS machine to run our Python lesson today. This will download the data we will work with and the Pandas notebook. (You can copy and paste the entire block at once.)</p>
<pre><code>source /class/cs101/etc/venv/cse/bin/activate /class/cs101/etc/venv/cse/
cd ; cd Desktop
jupyter notebook
</code></pre>
</li>
<li><p>Find both notebooks and open them in the browser.</p></li>
<li><p>To execute a code cell, select it and press <code>Ctrl</code>+<code>Enter</code> (or <code>Shift</code>+<code>Enter</code> to advance).</p></li>
</ul>
<h1>
<a id="error-handling-pdb-etc" class="anchor" href="#error-handling-pdb-etc" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Error handling (<code>pdb</code>, etc.)</h1>
<h4>
<a id="apr-5-noon200-pm" class="anchor" href="#apr-5-noon200-pm" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Apr. 5, Noon–2:00 p.m.</h4>
<ul>
<li>
<p>Download the following files to your Desktop folder:</p>
<ul>
<li> <a href="./lessons/pdb.ipynb"><code>pdb.ipynb</code></a>
</li>
</ul>
</li>
<li>
<p>Open a new Terminal and run the setup code below to prepare your EWS machine to run our Python lesson today. This will download the data we will work with and the Pandas notebook. (You can copy and paste the entire block at once.)</p>
<pre><code>source /class/cs101/etc/venv/cse/bin/activate /class/cs101/etc/venv/cse/
cd ; cd Desktop
jupyter notebook
</code></pre>
</li>
<li><p>Find the notebook and open it in the browser.</p></li>
<li><p>To execute a code cell, select it and press <code>Ctrl</code>+<code>Enter</code> (or <code>Shift</code>+<code>Enter</code> to execute and advance).</p></li>
</ul>
<h1>
<a id="optimizing-numerics-cython-numba-f2py" class="anchor" href="#optimizing-numerics-cython-numba-f2py" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Optimizing numerics (Cython, Numba, <code>f2py</code>)</h1>
<h4>
<a id="apr-12-noon200-pm" class="anchor" href="#apr-12-noon200-pm" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Apr. 12, Noon–2:00 p.m.</h4>
<ul>
<li>
<p>Download the following files to your Desktop folder:</p>
<ul>
<li> <a href="./lessons/api.ipynb"><code>api.ipynb</code></a>
</li>
<li> <a href="./lessons/numba.ipynb"><code>numba.ipynb</code></a>
</li>
</ul>
</li>
<li><p>Follow other instructions as above.</p></li>
<li><p>Also, you should really check out the CSE Annual Meeting today, in NCSA. See you there!</p></li>
</ul>
<h1>
<a id="jupyter-notebook-formerly-ipython" class="anchor" href="#jupyter-notebook-formerly-ipython" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Jupyter notebook (formerly IPython)</h1>
<h4>
<a id="apr-19-noon200-pm" class="anchor" href="#apr-19-noon200-pm" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Apr. 19, Noon–2:00 p.m.</h4>
<ul>
<li>
<p>Download the following files to your Desktop folder:</p>
<ul>
<li> <a href="./lessons/ipython.ipynb"><code>ipython.ipynb</code></a>
</li>
<li> <a href="./lessons/jupyter.ipynb"><code>jupyter.ipynb</code></a>
</li>
</ul>
</li>
<li><p>Follow other instructions as above.</p></li>
</ul>
<h1>
<a id="considerations-in-making-a-major-project" class="anchor" href="#considerations-in-making-a-major-project" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Considerations in making a major project</h1>
<h4>
<a id="apr-26-noon200-pm" class="anchor" href="#apr-26-noon200-pm" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Apr. 26, Noon–2:00 p.m.</h4>
<p>This workshop will cover creating your own modules, cross-platform compatibility, working with multiple files, and other factors to take into account when going beyond a single script.</p>
<ul>
<li>
<p>Download the following file to your Desktop folder:</p>
<ul>
<li> <a href="./lessons/modules.ipynb"><code>modules.ipynb</code></a>
</li>
</ul>
</li>
<li><p>Follow other instructions as above.</p></li>
</ul>
</section>
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